Assessing vulnerability for future Zika virus outbreaks using seroprevalence data and environmental suitability maps.

The 2015-17 Zika virus (ZIKV) epidemic in the Americas subsided faster than expected and evolving population immunity was postulated to be the main reason. Herd immunization is suggested to occur around 60-70% seroprevalence, depending on demographic density and climate suitability. However, herd immunity was only documented for a few cities in South America, meaning a substantial portion of the population might still be vulnerable to a future Zika virus outbreak. The aim of our study was to determine the vulnerability of populations to ZIKV by comparing the environmental suitability of ZIKV transmission to the observed seroprevalence, based on published studies. Using a systematic search, we collected seroprevalence and geospatial data for 119 unique locations from 37 studies. Extracting the environmental suitability at each location and converting to a hypothetical expected seroprevalence, we were able to determine the discrepancy between observed and expected. This discrepancy is an indicator of vulnerability and divided into three categories: high risk, low risk, and very low risk. The vulnerability was used to evaluate the level of risk that each location still has for a ZIKV outbreak to occur. Of the 119 unique locations, 69 locations (58%) fell within the high risk category, 47 locations (39%) fell within the low risk category, and 3 locations (3%) fell within the very low risk category. The considerable heterogeneity between environmental suitability and seroprevalence potentially leaves a large population vulnerable to future infection. Vulnerability seems to be especially pronounced at the fringes of the environmental suitability for ZIKV (e.g. Sao Paulo, Brazil). The discrepancies between observed and expected seroprevalence raise the question: "why did the ZIKV epidemic stop with large populations unaffected?". This lack of understanding also highlights that future ZIKV outbreaks currently cannot be predicted with confidence.

Correction: Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.

[This corrects the article DOI: 10.1371/journal.pmed.1003793.].

Accounting for assay performance when estimating the temporal dynamics in SARS-CoV-2 seroprevalence in the U.S.

Reconstructing the incidence of SARS-CoV-2 infection is central to understanding the state of the pandemic. Seroprevalence studies are often used to assess cumulative infections as they can identify asymptomatic infection. Since July 2020, commercial laboratories have conducted nationwide serosurveys for the U.S. CDC. They employed three assays, with different sensitivities and specificities, potentially introducing biases in seroprevalence estimates. Using models, we show that accounting for assays explains some of the observed state-to-state variation in seroprevalence, and when integrating case and death surveillance data, we show that when using the Abbott assay, estimates of proportions infected can differ substantially from seroprevalence estimates. We also found that states with higher proportions infected (before or after vaccination) had lower vaccination coverages, a pattern corroborated using a separate dataset. Finally, to understand vaccination rates relative to the increase in cases, we estimated the proportions of the population that received a vaccine prior to infection.

Limitations introduced by a low participation rate of SARS-CoV-2 seroprevalence data.

BACKGROUND: There has been a large influx of COVID-19 seroprevalence studies, but comparability between the seroprevalence estimates has been an issue because of heterogeneities in testing platforms and study methodology. One potential source of heterogeneity is the response or participation rate. METHODS: We conducted a review of participation rates (PR) in SARS-CoV-2 seroprevalence studies collected by SeroTracker and examined their effect on the validity of study conclusions. PR was calculated as the count of participants for whom the investigators had collected a valid sample, divided by the number of people invited to participate in the study. A multivariable beta generalized linear model with logit link was fitted to determine if the PR of international household and community-based seroprevalence studies was associated with the factors of interest, from 1 December 2019 to 10 March 2021. RESULTS: We identified 90 papers based on screening and were able to calculate the PR for 35 out of 90 papers (39%), with a median PR of 70% and an interquartile range of 40.92; 61% of the studies did not report PR. CONCLUSIONS: Many SARS-CoV-2 seroprevalence studies do not report PR. It is unclear what the median PR rate would be had a larger portion not had limitations in reporting. Low participation rates indicate limited representativeness of results. Non-probabilistic sampling frames were associated with higher participation rates but may be less representative. Standardized definitions of participation rate and data reporting necessary for the PR calculations are essential for understanding the representativeness of seroprevalence estimates in the population of interest.

Household Transmission Dynamics of Seasonal Human Coronaviruses.

BACKGROUND: Household transmission studies inform how viruses spread among close contacts, but few characterize household transmission of endemic coronaviruses. METHODS: We used data collected from 223 households with school-age children participating in weekly disease surveillance over 2 respiratory virus seasons (December 2015 to May 2017), to describe clinical characteristics of endemic human coronaviruses (HCoV-229E, HcoV-HKU1, HcoV-NL63, HcoV-OC43) infections, and community and household transmission probabilities using a chain-binomial model correcting for missing data from untested households. RESULTS: Among 947 participants in 223 households, we observed 121 infections during the study, most commonly subtype HCoV-OC43. Higher proportions of infected children (<19 years) displayed influenza-like illness symptoms than infected adults (relative risk, 3.0; 95% credible interval [CrI], 1.5-6.9). The estimated weekly household transmission probability was 9% (95% CrI, 6-13) and weekly community acquisition probability was 7% (95% CrI, 5-10). We found no evidence for differences in community or household transmission probabilities by age or symptom status. Simulations suggest that our study was underpowered to detect such differences. CONCLUSIONS: Our study highlights the need for large household studies to inform household transmission, the challenges in estimating household transmission probabilities from asymptomatic individuals, and implications for controlling endemic CoVs.

Disparities Made Invisible: Gaps in COVID-19 Data for American Indian and Alaska Native Populations.

Introduction: Complete COVID-19 data for American Indian/Alaska Native (AI/AN) populations are critical to equitable pandemic response. Methods: We used the COVID-19 U.S. State Policy database to document gaps in COVID-19 data reporting for AI/AN people. Results: Sixty-four percent of states do not report AI/AN data for at least one COVID-19 health metric: cases, hospitalizations, deaths, or vaccinations. Discussion: The lack of AI/AN-specific data masks the disproportionate burden of COVID-19 and presents challenges to COVID-19 prevention, policy implementation, and health equity. Conclusions: Public-facing data disaggregated by race may facilitate rapid response COVID-19 research and policymaking to support AI/AN communities.

Reduced spread of influenza and other respiratory viral infections during the COVID-19 pandemic in southern Puerto Rico.

INTRODUCTION: Impacts of COVID-19 mitigation measures on seasonal respiratory viruses is unknown in sub-tropical climates. METHODS: We compared weekly testing and test-positivity of respiratory infections in the 2019-2020 respiratory season to the 2012-2018 seasons in southern Puerto Rico using Wilcoxon signed rank tests. RESULTS: Compared to the average for the 2012-2018 seasons, test-positivity was significantly lower for Influenza A (p<0.001) & B (p<0.001), respiratory syncytial virus (RSV) (p<0.01), respiratory adenovirus (AdV) (p<0.05), and other respiratory viruses (p<0.001) following March 2020 COVID-19 stay at home orders. CONCLUSIONS: Mitigation measures and behavioral social distancing choices may have reduced respiratory viral spread in southern Puerto Rico.

Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.

BACKGROUND: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. METHODS AND FINDINGS: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. CONCLUSIONS: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

Predicting virologically confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007-2015 influenza seasons.

BACKGROUND: Children are important in community-level influenza transmission. School-based monitoring may inform influenza surveillance. METHODS: We used reported weekly confirmed influenza in Allegheny County during the 2007 and 2010-2015 influenza seasons using Pennsylvania's Allegheny County Health Department all-age influenza cases from health facilities, and all-cause and influenza-like illness (ILI)-specific absences from nine county school districts. Negative binomial regression predicted influenza cases using all-cause and illness-specific absence rates, calendar week, average weekly temperature, and relative humidity, using four cross-validations. RESULTS: School districts reported 2 184 220 all-cause absences (2010-2015). Three one-season studies reported 19 577 all-cause and 3012 ILI-related absences (2007, 2012, 2015). Over seven seasons, 11 946 confirmed influenza cases were reported. Absences improved seasonal model fits and predictions. Multivariate models using elementary school absences outperformed middle and high school models (relative mean absolute error (relMAE) = 0.94, 0.98, 0.99). K-5 grade-specific absence models had lowest mean absolute errors (MAE) in cross-validations. ILI-specific absences performed marginally better than all-cause absences in two years, adjusting for other covariates, but markedly worse one year. CONCLUSIONS: Our findings suggest seasonal models including K-5th grade absences predict all-age-confirmed influenza and may serve as a useful surveillance tool.

Estimating incidence of infection from diverse data sources: Zika virus in Puerto Rico, 2016.

Emerging epidemics are challenging to track. Only a subset of cases is recognized and reported, as seen with the Zika virus (ZIKV) epidemic where large proportions of infection were asymptomatic. However, multiple imperfect indicators of infection provide an opportunity to estimate the underlying incidence of infection. We developed a modeling approach that integrates a generic Time-series Susceptible-Infected-Recovered epidemic model with assumptions about reporting biases in a Bayesian framework and applied it to the 2016 Zika epidemic in Puerto Rico using three indicators: suspected arboviral cases, suspected Zika-associated Guillain-Barré Syndrome cases, and blood bank data. Using this combination of surveillance data, we estimated the peak of the epidemic occurred during the week of August 15, 2016 (the 33rd week of year), and 120 to 140 (50% credible interval [CrI], 95% CrI: 97 to 170) weekly infections per 10,000 population occurred at the peak. By the end of 2016, we estimated that approximately 890,000 (95% CrI: 660,000 to 1,100,000) individuals were infected in 2016 (26%, 95% CrI: 19% to 33%, of the population infected). Utilizing multiple indicators offers the opportunity for real-time and retrospective situational awareness to support epidemic preparedness and response.

Viral etiology and seasonal trends of pediatric acute febrile illness in southern Puerto Rico; a seven-year review.

BACKGROUND: Acute febrile illness (AFI) is an important cause for seeking health care among children. Knowledge of the most common etiologic agents of AFI and its seasonality is limited in most tropical regions. METHODOLOGY/PRINCIPAL FINDINGS: To describe the viral etiology of AFI in pediatric patients (≤18 years) recruited through a sentinel enhanced dengue surveillance system (SEDSS) in Southern Puerto Rico, we analyzed data for patients enrolled from 2012 to May 2018. To identify seasonal patterns, we applied time-series analyses to monthly arboviral and respiratory infection case data. We calculated coherence and phase differences for paired time-series to quantify the association between each time series. A viral pathogen was found in 47% of the 14,738 patients. Influenza A virus was the most common pathogen detected (26%). The incidence of Zika and dengue virus etiologies increased with age. Arboviral infections peaked between June and September throughout the times-series. Respiratory infections have seasonal peaks occurring in the fall and winter months of each year, though patterns vary by individual respiratory pathogen. CONCLUSIONS/SIGNIFICANCE: Distinct seasonal patterns and differences in relative frequency by age groups seen in this study can guide clinical and laboratory assessment in pediatric patients with AFI in Puerto Rico.

SARS-CoV-2 Transmission From People Without COVID-19 Symptoms.

Importance: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiology of coronavirus disease 2019 (COVID-19), is readily transmitted person to person. Optimal control of COVID-19 depends on directing resources and health messaging to mitigation efforts that are most likely to prevent transmission, but the relative importance of such measures has been disputed. Objective: To assess the proportion of SARS-CoV-2 transmissions in the community that likely occur from persons without symptoms. Design, Setting, and Participants: This decision analytical model assessed the relative amount of transmission from presymptomatic, never symptomatic, and symptomatic individuals across a range of scenarios in which the proportion of transmission from people who never develop symptoms (ie, remain asymptomatic) and the infectious period were varied according to published best estimates. For all estimates, data from a meta-analysis was used to set the incubation period at a median of 5 days. The infectious period duration was maintained at 10 days, and peak infectiousness was varied between 3 and 7 days (-2 and +2 days relative to the median incubation period). The overall proportion of SARS-CoV-2 was varied between 0% and 70% to assess a wide range of possible proportions. Main Outcomes and Measures: Level of transmission of SARS-CoV-2 from presymptomatic, never symptomatic, and symptomatic individuals. Results: The baseline assumptions for the model were that peak infectiousness occurred at the median of symptom onset and that 30% of individuals with infection never develop symptoms and are 75% as infectious as those who do develop symptoms. Combined, these baseline assumptions imply that persons with infection who never develop symptoms may account for approximately 24% of all transmission. In this base case, 59% of all transmission came from asymptomatic transmission, comprising 35% from presymptomatic individuals and 24% from individuals who never develop symptoms. Under a broad range of values for each of these assumptions, at least 50% of new SARS-CoV-2 infections was estimated to have originated from exposure to individuals with infection but without symptoms. Conclusions and Relevance: In this decision analytical model of multiple scenarios of proportions of asymptomatic individuals with COVID-19 and infectious periods, transmission from asymptomatic individuals was estimated to account for more than half of all transmissions. In addition to identification and isolation of persons with symptomatic COVID-19, effective control of spread will require reducing the risk of transmission from people with infection who do not have symptoms. These findings suggest that measures such as wearing masks, hand hygiene, social distancing, and strategic testing of people who are not ill will be foundational to slowing the spread of COVID-19 until safe and effective vaccines are available and widely used.

Using serological measures to estimate influenza incidence in the presence of secular trends in exposure and immuno-modulation of antibody response.

BACKGROUND: Influenza infection is often measured by a fourfold antibody titer increase over an influenza season (ie seroconversion). However, this approach may fail when influenza seasons are less distinct as it does not account for transient effects from recent infections. Here, we present a method to determine seroconversion for non-paired sera, adjusting for changes in individuals' antibody titers to influenza due to the transient impact of recent exposures, varied sampling times, and laboratory processes. METHODS: We applied our method using data for five H3N2 strains collected from 942 individuals, aged 2-90 years, during the first two study visits of the Fluscape cohort study (2009-2012) in Guangzhou, China. RESULTS: After adjustment, apparent seroconversion rates for non-circulating strains decreased while we observed a 20% increase in seroconversion rates to recently circulating strains. When examining seroconversion to the most recently circulating strain (A/Brisbane/20/2007) in our study, participants aged under 18, and over 64 had the highest seroconversion rates compared to other age groups. CONCLUSIONS: Our results highlight the need for improved methods when using antibody titers as an endpoint in settings where there is no clear influenza "off" season. Methods, like those presented here, that use titers from circulating and non-circulating strains may be key.

Early Insights from Statistical and Mathematical Modeling of Key Epidemiologic Parameters of COVID-19.

We report key epidemiologic parameter estimates for coronavirus disease identified in peer-reviewed publications, preprint articles, and online reports. Range estimates for incubation period were 1.8-6.9 days, serial interval 4.0-7.5 days, and doubling time 2.3-7.4 days. The effective reproductive number varied widely, with reductions attributable to interventions. Case burden and infection fatality ratios increased with patient age. Implementation of combined interventions could reduce cases and delay epidemic peak up to 1 month. These parameters for transmission, disease severity, and intervention effectiveness are critical for guiding policy decisions. Estimates will likely change as new information becomes available.

Epidemiologic and spatiotemporal trends of Zika Virus disease during the 2016 epidemic in Puerto Rico.

BACKGROUND: After Zika virus (ZIKV) emerged in the Americas, laboratory-based surveillance for arboviral diseases in Puerto Rico was adapted to include ZIKV disease. METHODS AND FINDINGS: Suspected cases of arboviral disease reported to Puerto Rico Department of Health were tested for evidence of infection with Zika, dengue, and chikungunya viruses by RT-PCR and IgM ELISA. To describe spatiotemporal trends among confirmed ZIKV disease cases, we analyzed the relationship between municipality-level socio-demographic, climatic, and spatial factors, and both time to detection of the first ZIKV disease case and the midpoint of the outbreak. During November 2015-December 2016, a total of 71,618 suspected arboviral disease cases were reported, of which 39,717 (55.5%; 1.1 cases per 100 residents) tested positive for ZIKV infection. The epidemic peaked in August 2016, when 71.5% of arboviral disease cases reported weekly tested positive for ZIKV infection. Incidence of ZIKV disease was highest among 20-29-year-olds (1.6 cases per 100 residents), and most (62.3%) cases were female. The most frequently reported symptoms were rash (83.0%), headache (64.6%), and myalgia (63.3%). Few patients were hospitalized (1.2%), and 13 (<0.1%) died. Early detection of ZIKV disease cases was associated with increased population size (log hazard ratio [HR]: -0.22 [95% confidence interval -0.29, -0.14]), eastern longitude (log HR: -1.04 [-1.17, -0.91]), and proximity to a city (spline estimated degrees of freedom [edf] = 2.0). Earlier midpoints of the outbreak were associated with northern latitude (log HR: -0.30 [-0.32, -0.29]), eastern longitude (spline edf = 6.5), and higher mean monthly temperature (log HR: -0.04 [-0.05, -0.03]). Higher incidence of ZIKV disease was associated with lower mean precipitation, but not socioeconomic factors. CONCLUSIONS: During the ZIKV epidemic in Puerto Rico, 1% of residents were reported to public health authorities and had laboratory evidence of ZIKV disease. Transmission was first detected in urban areas of eastern Puerto Rico, where transmission also peaked earlier. These trends suggest that ZIKV was first introduced to Puerto Rico in the east before disseminating throughout the island.

Recent influenza activity in tropical Puerto Rico has become synchronized with mainland US.

BACKGROUND: We used data from the Sentinel Enhanced Dengue Surveillance System (SEDSS) to describe influenza trends in southern Puerto Rico during 2012-2018 and compare them to trends in the United States. METHODS: Patients with fever onset ≤ 7 days presenting were enrolled. Nasal/oropharyngeal swabs were tested for influenza A and B viruses by PCR. Virologic data were obtained from the US World Health Organization (WHO) Collaborating Laboratories System and the National Respiratory and Enteric Virus Surveillance System (NREVSS). We compared influenza A and B infections identified from SEDSS and WHO/NREVSS laboratories reported by US Department of Health and Human Services (HHS) region using time series decomposition methods, and analysed coherence of climate and influenza trends by region. RESULTS: Among 23,124 participants, 9% were positive for influenza A and 5% for influenza B. Influenza A and B viruses were identified year-round, with no clear seasonal patterns from 2012 to 2015 and peaks in December-January in 2016-2017 and 2017-2018 seasons. Influenza seasons in HHS regions were relatively synchronized in recent years with the seasons in Puerto Rico. We observed high coherence between absolute humidity and influenza A and B virus in HHS regions. In Puerto Rico, coherence was much lower in the early years but increased to similar levels to HHS regions by 2017-2018. CONCLUSIONS: Influenza seasons in Puerto Rico have recently become synchronized with seasons in US HHS regions. Current US recommendations are for everyone 6 months and older to receive influenza vaccination by the end of October seem appropriate for Puerto Rico.

A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern.

INTRODUCTION: Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016-2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionable the information produced by these studies was. METHODS: To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE, and grey literature review, we identified studies that forecasted, predicted, or simulated ecological or epidemiological phenomena related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility, and clarity by independent reviewers. RESULTS: 2034 studies were identified, of which n = 73 met the eligibility criteria. Spatial spread, R0 (basic reproductive number), and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%), and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%), and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions, and 54% provided sufficient methodological detail to allow complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median of 119 days sooner than journal publication dates, they were used in only 30% of studies. CONCLUSIONS: Many ZIKV predictions were published during the 2016-2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics, and pandemics.

Age- and sex-related risk factors for influenza-associated mortality in the United States between 1997-2007.

Limited information on age- and sex-specific estimates of influenza-associated death with different underlying causes is currently available. We regressed weekly age- and sex-specific US mortality outcomes underlying several causes between 1997 and 2007 to incidence proxies for influenza A/H3N2, A/H1N1, and B that combine data on influenza-like illness consultations and respiratory specimen testing, adjusting for seasonal baselines and time trends. Adults older than 75 years of age had the highest average annual rate of influenza-associated mortality, with 141.15 deaths per 100,000 people (95% confidence interval (CI): 118.3, 163.9), whereas children under 18 had the lowest average mortality rate, with 0.41 deaths per 100,000 people (95% CI: 0.23, 0.60). In addition to respiratory and circulatory causes, mortality with underlying cancer, diabetes, renal disease, and Alzheimer disease had a contribution from influenza in adult age groups, whereas mortality with underlying septicemia had a contribution from influenza in children. For adults, within several age groups and for several underlying causes, the rate of influenza-associated mortality was somewhat higher in men than in women. Of note, in men 50-64 years of age, our estimate for the average annual rate of influenza-associated cancer mortality per 100,000 persons (1.90, 95% CI: 1.20, 2.62) is similar to the corresponding rate of influenza-associated respiratory deaths (1.81, 95% CI: 1.42, 2.21). Age, sex, and underlying health conditions should be considered when planning influenza vaccination and treatment strategies.

The role of disease surveillance in achieving IHR compliance by 2012.

The World Health Organization's revised International Health Regulations (IHR (2005)) call for member state compliance by mid-2012. Variation in disease surveillance and core public health capacities will affect each member state's ability to meet this deadline. We report on topics presented at the preconference workshop, "The Interaction of Disease Surveillance and the International Health Regulations," held at the 2010 International Society for Disease Surveillance conference in Park City, Utah. Presenters were from the Pan American Health Organization (PAHO), the U.S. Department of Health and Human Services (HHS), the Centers for Disease Control and Prevention (CDC), the Armed Forces Health Surveillance Center, U.S. Naval Research Unit Six, the Philippines' National Epidemiologic Center, and the French armed forces. The topics addressed were: an overview of the revised IHRs; disease surveillance systems implemented in Peru, the Philippines, and by the French armed forces; the capacity building efforts of the CDC; partnerships and contributions to IHR compliance from HHS; and the application of the IHRs to special populations. Results from the meeting evaluation indicate that many participants found the information useful in better understanding current efforts of the U.S. government and international organizations, areas for collaboration, and how the IHRs apply to their countries' public health systems. Topics to address at future workshops include progress and challenges to IHR implementation across all member states and additional examples of how disease surveillance supports the IHRs in resource-constrained countries. The preconference workshop provided the opportunity to convene public health experts from all regions of the world. Stronger collaborations and support to better detect and respond to public health events through building sustainable disease surveillance systems will not only help member states to meet IHR compliance by 2012, but will also improve pandemic preparedness and global health security.

Are leprosy reactions autoinflammatory diseases?

There are two types of leprosy reactions: reversal reactions or type 1 and erythema nodosum leprosum or type 2. Deformity and disability associated with leprosy are frequently the result of uncontrolled or untreated reactions. Although there is current availability of glucocorticoids as the mainstay of therapy, much needs to be learned about the etiology, risk factors, and pathogenesis of leprosy reactions. There is some evidence that leprosy reactions may represent, particularly, erythema nodosum leprosum autoinflammatory disease due to the aberrant activation of the innate immune system. The role for herpesviruses influencing autophagy in macrophages needs to be evaluated in the pathogenesis of leprosy reactions.